Scholarlink

<p>The multi-environment data of rice varieties was recorded for days to 50% flowering (DF) and grain yield (GY) during the kharif cropping seasons of 2016 to 2020 from five locations viz., Khudwani (KHD), Almora (ALM), Malan (MLN), Wadura (WAD) and Pombay (POM). These varieties were bred for medium to high altitudes (900 to 1900&#xa0;m amsl) of India. The locations belong to three Indian hill states viz., Jammu &amp; Kashmir (J&amp;K), Himachal Pradesh (HP) and Uttarakhand (UK). The group of genotypes from J&amp;K was designated as KHDVAR while those from HP and UK as MLNVAR and ALMVAR, respectively named after the breeding centres. The other genotypes were national check (NC), Zonal check (ZC) and local check (LC). The data was analysed using Additive Mean Multiplicative Interaction (AMMI), Genotype &amp; Genotype-Environment Interaction (GGE) and Best Linear Unbiased Prediction (BLUP). Combined ANOVA indicated significant (<i>P</i> &lt; 0.01) effects of genotype, environment, and GEI for both traits, with the environment contributing the largest proportion of total variation (33% for GY). AMMI analysis revealed that IPC1 captured most of the GEI sum of squares (92.7% for GY), effectively explaining the interaction structure. AMMI biplots identified LC, ZC, and KHDVAR as superior performers in KHD and WAD, whereas MLNVAR and ALMVAR were better adapted to MLN and ALM. GGE biplot analysis grouped the test sites into two mega-environments(ME), with ZC and LC emerging as winning genotypes in ME-I (KHD, POM, WAD) and NC in ME-II (MLN, ALM). Stability analysis indicated ALMVAR as the most stable genotype, while NC was highly unstable for both traits. BLUP predictions further confirmed LC (5.29 t/ha) and ZC (5.05 t/ha) as the highest yielders, and KHDVAR as broadly adapted. Overall, the integration of AMMI, GGE, and BLUP approaches enabled robust identification of high-yielding, stable, and environment-specific genotypes, with LC, ZC, and KHDVAR emerging as promising candidates for cultivation in the North-Western Himalayan region.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-Environment Analysis of Rice (Oryza Sativa L.) Genotypes Under Indian Hill Ecologies

  • 期刊论文

  • 发表日期：

    2026年3月30日

查看全文
  • Najeeb R. Sofi,
  • M. S. Dar,
  • Saba Mir,
  • Asif B. Shikari,
  • Neelam Bharadwaj,
  • J. P. Aditya,
  • Daisy Basanadrai,
  • Gazala H. Khan,
  • Ashaq Hussain,
  • Nakeeb-Un- Nisa,
  • Zafir Naik

摘要

The multi-environment data of rice varieties was recorded for days to 50% flowering (DF) and grain yield (GY) during the kharif cropping seasons of 2016 to 2020 from five locations viz., Khudwani (KHD), Almora (ALM), Malan (MLN), Wadura (WAD) and Pombay (POM). These varieties were bred for medium to high altitudes (900 to 1900 m amsl) of India. The locations belong to three Indian hill states viz., Jammu & Kashmir (J&K), Himachal Pradesh (HP) and Uttarakhand (UK). The group of genotypes from J&K was designated as KHDVAR while those from HP and UK as MLNVAR and ALMVAR, respectively named after the breeding centres. The other genotypes were national check (NC), Zonal check (ZC) and local check (LC). The data was analysed using Additive Mean Multiplicative Interaction (AMMI), Genotype & Genotype-Environment Interaction (GGE) and Best Linear Unbiased Prediction (BLUP). Combined ANOVA indicated significant (P < 0.01) effects of genotype, environment, and GEI for both traits, with the environment contributing the largest proportion of total variation (33% for GY). AMMI analysis revealed that IPC1 captured most of the GEI sum of squares (92.7% for GY), effectively explaining the interaction structure. AMMI biplots identified LC, ZC, and KHDVAR as superior performers in KHD and WAD, whereas MLNVAR and ALMVAR were better adapted to MLN and ALM. GGE biplot analysis grouped the test sites into two mega-environments(ME), with ZC and LC emerging as winning genotypes in ME-I (KHD, POM, WAD) and NC in ME-II (MLN, ALM). Stability analysis indicated ALMVAR as the most stable genotype, while NC was highly unstable for both traits. BLUP predictions further confirmed LC (5.29 t/ha) and ZC (5.05 t/ha) as the highest yielders, and KHDVAR as broadly adapted. Overall, the integration of AMMI, GGE, and BLUP approaches enabled robust identification of high-yielding, stable, and environment-specific genotypes, with LC, ZC, and KHDVAR emerging as promising candidates for cultivation in the North-Western Himalayan region.